Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems. (arXiv:2210.05931v1 [cs.LG])

Design time uncertainty poses an important challenge when developing a
self-adaptive system. As an example, defining how the system should adapt when
facing a new environment state, requires understanding the precise effect of an
adaptation, which may not be known at design time. Online reinforcement
learning, i.e., employing reinforcement learning (RL) at runtime, is an
emerging approach to realizing self-adaptive systems in the presence of design
time uncertainty. By using Online RL, the self-adaptive system can learn from
actual operational …

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